Overview

Dataset statistics

Number of variables7
Number of observations660
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.2 KiB
Average record size in memory56.2 B

Variable types

Numeric6
Categorical1

Alerts

skurczowe BP is highly correlated with rozkurczowe BP and 1 other fieldsHigh correlation
rozkurczowe BP is highly correlated with skurczowe BP and 1 other fieldsHigh correlation
tetnicze BP is highly correlated with skurczowe BP and 1 other fieldsHigh correlation
skurczowe BP is highly correlated with rozkurczowe BP and 1 other fieldsHigh correlation
rozkurczowe BP is highly correlated with skurczowe BP and 1 other fieldsHigh correlation
tetnicze BP is highly correlated with skurczowe BP and 1 other fieldsHigh correlation
skurczowe BP is highly correlated with rozkurczowe BP and 1 other fieldsHigh correlation
rozkurczowe BP is highly correlated with skurczowe BP and 1 other fieldsHigh correlation
tetnicze BP is highly correlated with skurczowe BP and 1 other fieldsHigh correlation
tetno is highly correlated with skurczowe BPHigh correlation
skurczowe BP is highly correlated with tetno and 2 other fieldsHigh correlation
rozkurczowe BP is highly correlated with skurczowe BP and 1 other fieldsHigh correlation
tetnicze BP is highly correlated with skurczowe BP and 1 other fieldsHigh correlation
szerokosc SAS is highly correlated with srednie HbO2High correlation
srednie HbO2 is highly correlated with szerokosc SASHigh correlation
hipoksja is uniformly distributed Uniform
skurczowe BP has unique values Unique
rozkurczowe BP has unique values Unique
tetnicze BP has unique values Unique
szerokosc SAS has unique values Unique
srednie HbO2 has unique values Unique

Reproduction

Analysis started2022-09-01 14:41:09.141791
Analysis finished2022-09-01 14:41:16.554886
Duration7.41 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

tetno
Real number (ℝ≥0)

HIGH CORRELATION

Distinct95
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.09890388
Minimum43.16546763
Maximum76.92307692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-09-01T16:41:16.906789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum43.16546763
5-th percentile49.18032787
Q155.55555556
median61.8556701
Q368.96551724
95-th percentile75
Maximum76.92307692
Range33.7576093
Interquartile range (IQR)13.40996169

Descriptive statistics

Standard deviation8.27351159
Coefficient of variation (CV)0.1332312017
Kurtosis-1.125675583
Mean62.09890388
Median Absolute Deviation (MAD)7.109847138
Skewness-0.06199617712
Sum40985.27656
Variance68.45099403
MonotonicityNot monotonic
2022-09-01T16:41:16.997162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.9655172429
 
4.4%
71.4285714325
 
3.8%
58.8235294122
 
3.3%
69.7674418621
 
3.2%
72.2891566320
 
3.0%
59.4059405920
 
3.0%
67.4157303420
 
3.0%
68.1818181820
 
3.0%
70.5882352919
 
2.9%
61.855670118
 
2.7%
Other values (85)446
67.6%
ValueCountFrequency (%)
43.165467631
 
0.2%
45.112781951
 
0.2%
45.454545451
 
0.2%
45.801526721
 
0.2%
46.511627913
0.5%
46.8751
 
0.2%
47.058823531
 
0.2%
47.244094493
0.5%
47.619047622
 
0.3%
485
0.8%
ValueCountFrequency (%)
76.923076926
 
0.9%
76.433121021
 
0.2%
75.9493670912
1.8%
75.471698111
 
0.2%
7516
2.4%
74.534161493
 
0.5%
74.0740740713
2.0%
73.61963192
 
0.3%
73.1707317117
2.6%
72.727272732
 
0.3%

skurczowe BP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct660
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.03251749
Minimum41.39259742
Maximum87.87393253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-09-01T16:41:17.097937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum41.39259742
5-th percentile50.45717565
Q160.51474035
median68.03360369
Q373.68265144
95-th percentile81.89428607
Maximum87.87393253
Range46.48133511
Interquartile range (IQR)13.16791109

Descriptive statistics

Standard deviation9.72806061
Coefficient of variation (CV)0.1451245004
Kurtosis-0.3161707123
Mean67.03251749
Median Absolute Deviation (MAD)6.762715466
Skewness-0.3024797023
Sum44241.46155
Variance94.63516323
MonotonicityNot monotonic
2022-09-01T16:41:17.198911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.248413261
 
0.2%
69.279048831
 
0.2%
70.48902051
 
0.2%
70.417915071
 
0.2%
70.685223631
 
0.2%
71.209316191
 
0.2%
78.198969351
 
0.2%
78.365847891
 
0.2%
79.161589311
 
0.2%
81.893951141
 
0.2%
Other values (650)650
98.5%
ValueCountFrequency (%)
41.392597421
0.2%
41.910161021
0.2%
42.083451191
0.2%
42.101166241
0.2%
42.754823461
0.2%
42.84442541
0.2%
42.948946381
0.2%
43.094337431
0.2%
43.194807311
0.2%
43.35037831
0.2%
ValueCountFrequency (%)
87.873932531
0.2%
87.844185231
0.2%
87.563169811
0.2%
87.520015831
0.2%
87.363087571
0.2%
86.925248651
0.2%
86.899319161
0.2%
86.780499231
0.2%
86.41436921
0.2%
86.128619271
0.2%

rozkurczowe BP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct660
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.3833156
Minimum78.34576321
Maximum161.9201493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-09-01T16:41:17.299394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum78.34576321
5-th percentile92.06267087
Q1107.1828737
median116.3881945
Q3126.5127754
95-th percentile138.4130858
Maximum161.9201493
Range83.5743861
Interquartile range (IQR)19.32990167

Descriptive statistics

Standard deviation14.37312453
Coefficient of variation (CV)0.1234981531
Kurtosis0.2857186501
Mean116.3833156
Median Absolute Deviation (MAD)9.495046928
Skewness0.1950753939
Sum76812.98828
Variance206.5867087
MonotonicityNot monotonic
2022-09-01T16:41:17.398558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110.99982521
 
0.2%
120.70424261
 
0.2%
122.96902021
 
0.2%
124.00363111
 
0.2%
123.91494951
 
0.2%
124.1416821
 
0.2%
130.17365971
 
0.2%
131.40388191
 
0.2%
131.30877051
 
0.2%
135.60169211
 
0.2%
Other values (650)650
98.5%
ValueCountFrequency (%)
78.345763211
0.2%
78.634985241
0.2%
81.348399511
0.2%
84.866279821
0.2%
85.593096991
0.2%
85.849326371
0.2%
85.882293261
0.2%
87.133307391
0.2%
87.374351931
0.2%
87.482007511
0.2%
ValueCountFrequency (%)
161.92014931
0.2%
160.66024951
0.2%
160.55494461
0.2%
159.71271721
0.2%
158.90214711
0.2%
158.77798481
0.2%
158.7439691
0.2%
157.50545681
0.2%
157.40240931
0.2%
156.50677811
0.2%

tetnicze BP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct660
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.48278352
Minimum55.0155066
Maximum112.5560048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-09-01T16:41:17.493023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum55.0155066
5-th percentile64.88670863
Q176.31064712
median84.94457836
Q390.49826063
95-th percentile99.63489351
Maximum112.5560048
Range57.54049819
Interquartile range (IQR)14.18761351

Descriptive statistics

Standard deviation10.76952162
Coefficient of variation (CV)0.1290029053
Kurtosis-0.05283795502
Mean83.48278352
Median Absolute Deviation (MAD)6.754111137
Skewness-0.2333150072
Sum55098.63712
Variance115.9825959
MonotonicityNot monotonic
2022-09-01T16:41:17.584918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.498883911
 
0.2%
86.420780071
 
0.2%
87.982353751
 
0.2%
88.27982041
 
0.2%
88.428465581
 
0.2%
88.853438131
 
0.2%
95.523866131
 
0.2%
96.045192571
 
0.2%
96.543983021
 
0.2%
99.796531461
 
0.2%
Other values (650)650
98.5%
ValueCountFrequency (%)
55.01550661
0.2%
56.211507071
0.2%
56.536204091
0.2%
57.273240991
0.2%
57.288611511
0.2%
57.397817051
0.2%
57.693241521
0.2%
57.936527761
0.2%
58.235138731
0.2%
58.25810121
0.2%
ValueCountFrequency (%)
112.55600481
0.2%
111.90009371
0.2%
111.89376141
0.2%
111.20944081
0.2%
111.17045181
0.2%
110.77966111
0.2%
110.52423581
0.2%
110.41763551
0.2%
109.58800551
0.2%
109.34647721
0.2%

szerokosc SAS
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct660
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0002298157055
Minimum-0.01035368704
Maximum0.0103473397
Zeros0
Zeros (%)0.0%
Negative359
Negative (%)54.4%
Memory size5.3 KiB
2022-09-01T16:41:17.688931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.01035368704
5-th percentile-0.004236116333
Q1-0.001336040515
median-0.0001459726752
Q30.00108502036
95-th percentile0.003544852551
Maximum0.0103473397
Range0.02070102674
Interquartile range (IQR)0.002421060874

Descriptive statistics

Standard deviation0.002368746594
Coefficient of variation (CV)-10.30715716
Kurtosis2.442260148
Mean-0.0002298157055
Median Absolute Deviation (MAD)0.001218700201
Skewness-0.1433080827
Sum-0.1516783656
Variance5.610960425 × 10-6
MonotonicityNot monotonic
2022-09-01T16:41:17.798285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0050921465271
 
0.2%
-0.00081957447361
 
0.2%
0.0002377392681
 
0.2%
-0.00017272719561
 
0.2%
-0.0011983739671
 
0.2%
-0.00017788380231
 
0.2%
-0.0017533697661
 
0.2%
0.00036362387811
 
0.2%
0.0013473591581
 
0.2%
-0.00071630840211
 
0.2%
Other values (650)650
98.5%
ValueCountFrequency (%)
-0.010353687041
0.2%
-0.0090689798781
0.2%
-0.0085401403561
0.2%
-0.0080269310921
0.2%
-0.0076985242961
0.2%
-0.0073722094771
0.2%
-0.0071731324271
0.2%
-0.0069734052131
0.2%
-0.0068357298411
0.2%
-0.0066889940381
0.2%
ValueCountFrequency (%)
0.01034733971
0.2%
0.01007543121
0.2%
0.006902447031
0.2%
0.0065352828591
0.2%
0.0064464385291
0.2%
0.0064026373241
0.2%
0.0060542637991
0.2%
0.0058962852731
0.2%
0.0058546634241
0.2%
0.0054625592931
0.2%

srednie HbO2
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct660
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.04136919827
Minimum-1.834475928
Maximum2.022632405
Zeros0
Zeros (%)0.0%
Negative373
Negative (%)56.5%
Memory size5.3 KiB
2022-09-01T16:41:17.898310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.834475928
5-th percentile-0.6470425602
Q1-0.2739421024
median-0.0451916643
Q30.1543275207
95-th percentile0.6468357403
Maximum2.022632405
Range3.857108333
Interquartile range (IQR)0.4282696231

Descriptive statistics

Standard deviation0.4199051758
Coefficient of variation (CV)-10.15018887
Kurtosis2.657407563
Mean-0.04136919827
Median Absolute Deviation (MAD)0.215466718
Skewness0.3099085902
Sum-27.30367086
Variance0.1763203567
MonotonicityNot monotonic
2022-09-01T16:41:17.995332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13445526431
 
0.2%
0.47627665011
 
0.2%
-0.26965963611
 
0.2%
0.15233943321
 
0.2%
0.47227438231
 
0.2%
0.085580517411
 
0.2%
-0.31033032551
 
0.2%
-1.3185571421
 
0.2%
0.27430485111
 
0.2%
0.58137334981
 
0.2%
Other values (650)650
98.5%
ValueCountFrequency (%)
-1.8344759281
0.2%
-1.3487317451
0.2%
-1.348386191
0.2%
-1.3369281821
0.2%
-1.3185571421
0.2%
-1.1963714761
0.2%
-1.14345821
0.2%
-1.1304269961
0.2%
-1.1291788851
0.2%
-1.0932599931
0.2%
ValueCountFrequency (%)
2.0226324051
0.2%
1.8177939611
0.2%
1.7418730491
0.2%
1.348734161
0.2%
1.1936202681
0.2%
1.1690746261
0.2%
1.1569133561
0.2%
0.99102493041
0.2%
0.98885423981
0.2%
0.95610541891
0.2%

hipoksja
Categorical

UNIFORM

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
330 
1
330 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters660
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0330
50.0%
1330
50.0%

Length

2022-09-01T16:41:18.084352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-01T16:41:18.162575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0330
50.0%
1330
50.0%

Most occurring characters

ValueCountFrequency (%)
0330
50.0%
1330
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number660
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0330
50.0%
1330
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0330
50.0%
1330
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0330
50.0%
1330
50.0%

Interactions

2022-09-01T16:41:15.842948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:12.375211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:13.947962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.428539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.889343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.364692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.925653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:12.653149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.022959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.508454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.965991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.445721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:16.006951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:12.926300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.103425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.582090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.039695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.518464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:16.082693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:13.212514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.184664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.660496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.121175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.598827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:16.165640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:13.489290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.255899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.734448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.199588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.679522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:16.246050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:13.765839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.345023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:14.806299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.279931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-01T16:41:15.759891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-01T16:41:18.222634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-01T16:41:18.327304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-01T16:41:18.433781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-01T16:41:18.547442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-01T16:41:16.368552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-01T16:41:16.483410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

tetnoskurczowe BProzkurczowe BPtetnicze BPszerokosc SASsrednie HbO2hipoksja
056.33802865.248413110.99982580.4988840.0050920.1344550
151.72413865.941506112.41563881.432883-0.0043300.4514720
249.18032863.267139113.06253279.8656030.006446-0.5668810
350.42016863.358812112.37387479.6971660.0011060.1331770
455.04587261.381638111.30431778.022531-0.0066120.5134240
549.18032863.230620116.27239180.911210-0.0015860.5124960
648.78048861.725775112.33692578.596158-0.006973-0.1140280
751.28205161.075887112.79940378.3170590.006902-0.5844360
850.20920560.956475110.97600577.6296520.0009880.4462110
951.28205160.085017107.53656675.902200-0.000898-0.2958600

Last rows

tetnoskurczowe BProzkurczowe BPtetnicze BPszerokosc SASsrednie HbO2hipoksja
65075.94936760.982006100.05990174.007971-0.000014-0.0085251
65174.07407459.43825297.06298171.9798290.000125-0.2075361
65272.28915760.30827198.86007773.158873-0.0000330.1687571
65368.96551760.43247998.86827373.2444100.000346-0.3025681
65475.00000060.075340100.44316273.531281-0.000684-0.1842691
65572.28915758.84267998.35221672.012525-0.000446-0.0088571
65672.28915759.06077599.59829472.5732810.001490-0.3430271
65773.17073260.142736102.33377974.2064170.000098-0.0430341
65873.17073259.936224101.31393373.728794-0.0007360.0496081
65974.07407460.009439100.62755973.548813-0.000877-0.0933391